an economic evaluation of deep tillage to reduce soil compaction on crop-livestock farms in western...

Agricultural Systems 37 (1991) 291-307

An Economic Evaluation of Deep Tillage to Reduce Soil Compaction on Crop-Livestock Farms in

Western Australia

Amir Abadi Ghadim Geraldton District Office, Western Australian Department of Agriculture,

Geraldton, Western Australia 6530

Ross Kingwell Western Australian Department of Agriculture,

Baron-Hay Court, South Perth, Western Australia 6t51

&

David Pannell School of Agriculture, University of Western Australia,

Crawley, Western Australia 6009

(Received 5 November 1990; accepted 24 May 1991)

ABSTRACT

Deep tillage can ameliorate soil compaction caused by movement of heavy machinery on sandy soils. A whole-farm linear programming model MIDAS, is used to assess the economics of deep tillage in Western Aus- tralia's northern wheatbelt.

Model results indicate that deep tillage is likely to result in modest profit improvements in the region. The size of the increase in profit is shown to depend on the size of yieM increases from deep tillage and the appropriate selection of rotations, frequency and timing of deep tillage and the type and size of deep tillage machinery.

In general if the expected yieM response to deep tillage is at least 300 kg/ha in cereal-lupin rotations on the yellow sandplain soils, adoption of deep tillage is shown to be profitable. Model results also indicate the preferred periods for deep tillage to be in summer (after cyclonic rains or thunderstorms) or during the five days immediately following completion of seeding in winter. Several reasons are offered as to why deep tillage has not been adopted as widely or as quickly as had been anticipated by some.

291 Agricultural Systems 0308-521X/91/$03.50 © 1991 Elsevier Science Publishers Ltd, England. Printed in Great Britain

292 A. K. Abadi Ghadim, R. S. Kingwell, D. J. Pannell

INTRODUCTION

In many agricultural systems, repeated passage of heavy machinery leads to soil compaction (Soane, 1970). In the northern wheatbelt of Western Australia, it results in the formation of hard pans on very sandy soils which dominate much of the region (Henderson, 1986). The hard pans are very dense sub-surface layers, often 20-30 cm below the soil surface. They inhibit root penetration and prevent access by plants to moisture stored deeper in the soil profile (Hamblin & Tennant, 1979; Bowden & Jarvis, 1985). Hard pans also reduce nitrogen uptake by the crop (Delroy & Bowden, 1986) and reduce the water storage capacity of the soil (Mickelson, 1983).

One response to this problem is deep tillage (also referred to as deep ripping or sub-soiling) (Mickelson, 1983; Voorhees, 1983; Chaudhary et al., 1985). Field trials and farmer experience in the northern wheatbelt have shown that deep tillage can increase wheat yields by up to to 560 kg/ha (Bowden, 1982; Jarvis, 1983, 1986; Hamblin, 1986). This increase is approximately 40% of the average yield obtained without deep tillage, so researchers expected that adoption of deep tillage would be widespread and rapid. In practice, however, adoption of deep tillage technology has been much slower than anticipated (Jarvis, 1990).

In this paper MIDAS, a whole-farm bioeconomic model, is used to evaluate deep tillage in Western Australia's northern wheatbelt. The model represents a wide range of deep tillage options including different crop-pasture rotations, different areas of deep tillage and different timing of deep tillage. The aims of this paper are to identify: (a) the yield response required for the benefits of deep tillage to outweigh its costs; (b) which of the many alternative methods of implementing deep tillage are likely to be economically preferred, and (c) possible reasons for the lower than expected adoption of deep tillage.

The next section of the paper is a description of the farming system. Following that are details of MIDAS, the whole-farm model used in the analysis. Results are presented and discussed and the paper ends with a summary of the main conclusions.

THE FARMING SYSTEM

The northern wheatbelt region of Western Australia (Fig. 1) experiences a Mediterranean climate with an annual average rainfall of 340 mm, 75% of which falls between May and October. Crops are sown in May-June and are harvested in November-December. Crops include wheat, lupins, barley, oats and field peas.

Effects of deep tillage on soil compaction 293

Fig. 1. The northern wheatbelt region of Western Australia.

The livestock enterprises on farms in the region consist almost entirely of sheep grown for wool, meat or export as live animals. Lambing is in late autumn or early winter and shearing is in spring or early autumn. Sheep graze annual pastures during winter and spring and on a combina- tion of crop residues and dry annual pasture in summer and autumn. The pastures contain volunteer annual grasses and herbs, and include

TABLE i Main soil classes in the northern wheatbelt region

Soil Description pH Range Class

Acid sands (S1) Yellow loamy sand < 5.5 Yellow sands ($2) Deep yellow loamy sands, some 6.0-7.0

with gravel or clay at depth Red ioams ($3) Red sandy loams 5.5~.5 Medium heavy ($4) Red-brown hard setting loams, 6.0-7.5

clay Ioams


introduced annual legumes, Trifolium subterraneum (subterranean clover) and Medicago spp. (annual medics).

Soils in the region can be classified into four broad categories, as described in Table 1. These soils have been subject to long histories of ap- plication of artificial fertilizers, particularly phosphate, nitrogen and trace element fertilizers.

Most farmland is cleared and arable. Farm operations are highly mech- anized and most farms remain family-owned and operated. Casual labour is hired only for a few months of the year to assist in key tasks such as crop sowing, harvesting and shearing. Almost all farms have a mix of crop and livestock enterprises that interact to form the farming system.

The crop establishment gear used on many farms is basically a four- wheel-drive tractor of around 150 hp with an air seeder capable of direct drilling around 124 ha per day on the sandy soils (S1, $2 and $3 in Table 1). Conventional harvesters are used on most crops, with spreader at- tachments used for cereals. The single-pass method of crop establishment means that approximately 9 t of gear passes over the soil when it may be most subject to compaction.

Rotation mixes involving cereals and lupins are often adopted on the deep yellow sands ($2) that are subject to hard pan formation. The crops on these sands are sown directly into old stubbles, there being no tillage operation to prepare the seedbed (Horwood & Green, 1991). Occasion- ally, at seeding, trailing harrows are used to provide an even seed bed. Weed control in cereal phases often currently consists of Spray-Seed ~ (Paraquat and Diquat) and Glean~ (Chlorsulphuron) at recommended rates. Weed control in lupin phases is usually Simazine ~ and Fusilade ~ applied at rates of 1.5 litres/ha and 0.25 litres/ha, respectively.

THE MODEL

MIDAS (Model of an Integrated Dryland Agricultural System) is a whole-farm linear programming (LP) model for crop-livestock farms in Western Australia. There are versions of MIDAS for a number of different regions. The version known as the Northern Wheatbelt Model (NWM) includes approximately 445 activities and 200 constraints. Earlier versions of MIDAS have been described by Morrison et al. (1986) and in greater detail by Kingweil (1987) and have been used in a broad range of applications (e.g. Pannell & Panetta, 1986; Kingwell & Pannell, 1987; Pannell & Falconer, 1988; Abadi Ghadim & Pannell, 1991). Only a brief


model description is given here; the reader is referred to the cited literature for more details. A copy of the model and full details of model parameters are available on request.

For the purposes of this study, the model was adjusted to represent a typical farm in the Eradu region. This region is dominated by the deep yellow sands on which hard pan formation reduces potential yield. The model farm includes 2500 ha, of which 90% is yellow sand and 10% is red loam soil. On yellow sands it is assumed that crops are direct drilled. On red loams, depending on the rotation, crops are either direct drilled or seeded after a single cultivation. The management options, rotations and soil types on the model farm are representative of almost all farms in the Eradu region.

Apart from deep tillage options, the major decision variables in the model are:

- - the sequence of crops and pastures on each soil class. Cereals can be grown continuously or rotated in one of a number of sequences with pastures or legumes crops;

--rates of nitrogen fertilizer on each soil type; -- t iming and duration of sowing for each crop species; --use of private contractors for seeding and harvesting;

sale or on-farm use of grain produced; - -use of crop stubbles for sheep feed; --sheep flock structure and timing of sales;

sheep stocking rate; --feed management across the growing season.

In collecting information for the NWM, a range of sources was used including, where possible, experimental and trial data. These data were sometimes amended or supplemented by subjective estimates supplied by advisers, researchers or consultants to ensure that the data were truly representative of an average farm in the region. Sensitivity analysis was undertaken to expose critical assumptions, occasionally leading to trial or survey work to refine the model.

A model solution represents a steady-state profit-maximizing farm plan showing values for each of the decision variables described above. The solution is underpinned by assumptions of an average season, fixed input and output prices, certain production relationships, risk-neutral management, fixed family labour and implicit leisure and soil conservation preferences.

The model is run on microcomputers using the MS-DOS operating system. Model parameters are displayed and edited using electronic spreadsheet templates. Solution of the LP model is by AESOP, a linear version of MINOS (Murtagh & Saunders, 1978). The authors have


developed a menu-driven package called MARG to simplify model solution (Pannell, 1990). MARG allows the user to easily revise the model, to generate a series of model solutions and to automatically create user-de- signed output tables. It also provides easy access to support-programs such as spreadsheets, text editors and graphics programs.

Inclusion of deep tillage in the model

Deep tillage options were included in the NWM in cereal phases of cereal-pasture, continuous cereal and cereal-lupin rotations of the yellow loamy sands. In these rotations deep tillage could be carried out during or after seeding, in summer or in a pasture phase. Deep tillage could be undertaken by the farmer or by a contactor and could be carried out for, at most, 5 days immediately after completion of seeding. Beyond 5 days after seeding, deep tillage may damage emerging cereal seedlings.

Where only a single tractor is owned, deep tillage carried out during the seeding programme requires part of the programme to be delayed, resulting in yield reductions due to shortening of the growing season. Trial evidence indicates the yield penalty is 15 kg/ha for each day seeding is delayed after the first day suitable for crop sowing. Calculation of yield penalties due to delays from deep tillage during the seeding programme is rather complex as it must account for the following considerations. The relative rates of seeding and deep tillage mean that it takes approximately 5 days to deep till the area sown in 2 days. Deep tillage inserted in the seeding programme should be timed as late as possible in the programme so as to minimize the area of crop affected by delays. However, it cannot be later than the third last day of seeding, since the last 2 days are deep tilled in the 5 days after seeding is completed. No more than 5 days of deep tillage can be inserted at this point because on a sixth day, tillage would be conducted on crop which has been sown for more than 5 days. Thus any further deep tillage requires on additional break in the seeding programme before the fourth last day of seeding.

The yield penalty (YP) is calculated as

Y P = LSP x SR X DSA

where LSP -- late seeding penalty (kg/ha/day), SR = seeding rate (ha/day) and

DSA = days of seeding affected (days).

For example, given LSP = 15 kg/ha/day, SR = 124 ha/day and DSA = 2 days, the yield penalty for inserting 1-5 days of deep tillage into the seeding programme is 3.72 tonnes per day spent on deep tillage. For 6-10 days of deep tillage, the days of seeding affected is doubled to 4,


so the yield penalty is 7.44 tonnes per extra day of deep tillage. In rotations such as cereal-lupin and continuous cereals, choices about

the frequency of ripping were represented. Deep tillage of cereal crops could be carried out either in every cereal phase or in every second cereal year. In practice, the latter meant a farmer would deep till half the area sown to cereals each year, assuming that farmers have different parts of their farm in different phases of the rotation.

The main cropping gear used for deep tillage in the NWM was a 150 hp four-wheel-drive tractor with an air seeder capable of direct drilling 124 ha/day. Crop yields on the compacted yellow sandplain soils were assumed to be 1.2 t/ha for wheat and 1-0 t/ha for lupins in an expected season. For deep tillage it was assumed that the existing tractor used an 11 tine Agrow-plough ~ tillage implement.

RESULTS AND DISCUSSION

Break-even yield responses

The model was solved under a range of scenarios to determine the yield response necessary for the benefits of deep tillage to outweigh the costs. The scenarios considered are different amounts of farm machinery, different areas of deep tillage, different levels of residual effect from ripping and different wheat prices.

Machinery size

To examine the impact of machinery size on the profitable selection of deep tillage, a single tractor system was contrasted with a two tractor system, and two sizes of tillage gear were compared.

Firstly, ownership of an additional tractor was contrasted against a single tractor, both systems having an l l-tine tillage implement. The two tractor system allowed deep tillage to immediately follow crop sowing. Additional labour was hired and the deep tillage gear was used 22 h/day.With this system, large areas could be deep tilled without causing delays in seeding. Results are shown in Fig. 2. The break-even yield at which the two tractor system is as profitable as no ripping is 350 kg/ha whereas for the single tractor system, deep tillage is first incorporated in an optimal farm plan when the yield response to ripping is 240 kg/ha. The need for the higher yield response in the two tractor system to justify selection of deep tillage arises from its higher operating costs caused by the hire of extra labour and its higher capital cost. The operating costs


Fig. 2.

Farm prof i t ($ ' 0 0 0 ) 11o

- "" NO RIPPING 1 TRACTOR * 11 TINE

90 2 TRACTORS + 11 TINE ~

70 1 T

50

30 ~ , 120 240 360 480 SO0

W h e a t yield response to ripping (kg/ha)

Effect of different deep tillage machinery complements on farm profit.

mean greater revenues from deep tillage are required to justify its adoption.

When the yield response to deep tillage is 600 kg/ha or less, the two tractor system is less profitable than the single tractor system. Given farmer experience that the yield response is likely to average around 400 kg/ha, investment in a second tractor would not be warranted in most cases.

Next, the impact of the size of deep tillage implement on farm profit is examined. The comparison was between a 200 hp tractor pulling a 19- fine implement and a smaller 150 hp tractor pulling an 11-fine implement. Results in Fig. 2. labelled '1 tractor + 19 fine', include the higher ownership and operating costs of the larger tractor and larger implement.

When the larger tractor and 19-tine implement are used, larger areas are deep tilled with less yield foregone through late sowing relative to the single tractor, 11-fine case (Fig. 2). At yield responses to deep tillage of 260 kg/ha or higher, farm plans based on the larger machinery are more profitable. Figure 2 also shows that the single tractor system with a large implement returns greater profit at all yield responses to deep tillage when compared to the two tractor system using the I 1-fine implement.

Size of deep tillage programme

Often adoption of an innovation by a farmer is in various stages. For example, with an innovation like deep tillage farmers may first test the innovation on a small area using borrowed gear, then purchase their own gear and finally gradually increase the area of deep tillage. The gradual introduction of the technology into the farming system raises the


Farm profit ($ '000)

120

100

80

60

40

Fig. 3.

..... NO RIPPING

RIP 1000 ha - ~ - RIP 250 ha

, o 0 J q r

120 240 360 480 600

Wheat yield r e s p o n s e to ripping (kg/ha)

Effect of size of the deep tillage programme on farm profit.

question as to what is an appropriate size of programme of deep tillage? To partly address this question the authors compared farm profitability of no deep tillage, to that given small (250 ha) and large (1000 ha) areas of deep tillage. The machinery in each case is a single tractor with an 11-tine implement. Farm profits are shown in Fig. 3.

In both cases, the break-even yield response is approximately 300 kg/ha but is slightly higher for the larger area of deep tillage. Notably, the effect of yield response on profit is much greater if the larger area is deep ripped. If the yield response exceeds 330 kg/ha, the larger area of deep ripping is clearly more profitable, while at yield responses below this value, losses are less for a smaller programme of deep tillage. Given that the response to deep tillage is quite variable from year to year, this result may explain some of the reluctance farmers have shown to adopting large programmes of deep tillage. Where farmers are risk averse or are experiencing cash-flow difficulties, they may be prepared to forego the benefits of a large programme of deep tillage in some years to avoid the costs associated with a large programme in years when deep tillage does not greatly increase yields.

Residual response from deep tillage

Results presented above are based on the assumption that there is a residual response to deep tillage in the second year after the initial operation. It was assumed that this residual response is only 10% of the initial response. However, farmer and research experience (WADA, 1985; Jarvis, 1990) indicate that residual responses for up to three extra years are possible and that they can exceed 10%.


Farm profit ($ '000) 100

90

80

7 0

60 -

50-

40 120

i /

- N O R I P P I N G / 7 : - .

- - ~ - L O W R E S I D U A L , 1 Y E A R i/: /" /-" j

HIGH RESIDUAL,3YEARS /

/ . . / / / .~

/ / / " i

/ / . - / /

I I r

240 360 480 600

Wheat yield response to ripping (kg/ha)

Fig. 4. Effect of size and duration of residual response to deep tillage on farm profit.

The residual yield response in the NWM was altered to allow residual effects to last up to 3 years depending on the rotation. The magnitude of the residual response was assumed to follow the function (Yi/( t + 1)) where Yi is the initial yield response and t is the number of years after the initial deep tillage. Results in Fig. 4 show that if the residual response lasts for 3 years, then relatively higher farm profits can be realized when compared to a shorter and lower residual response. In addition, a longer duration of residual response reduces the break-even yield response and facilitates greater adoption of deep tillage at lower levels of initial yield response to deep tillage.

Wheat price

As part of the examination of the sensitivity of selection of deep tillage in optimal farm plans, the price of wheat was varied from Australian $152 to $130 per tonne. Figure 5 illustrates the impact of wheat price on the break-even yield response to deep tillage.

The break-even yield response is slightly lower at the higher wheat price. This is because at the higher wheat price the value of a given yield increase is greater, so a smaller increase is needed to offset the cost of obtaining the extra yield. This means that in the absence of any price support systems in Australia, the downturn in world wheat prices in 1990 would have reduced farmers' incentive to adopt deep tillage.

In the scenarios examined, it is remarkable that in almost all cases the break-even yield falls between 260 and 350 kg/ha. Most commonly it is around 300 kg/ha. Although field experiments indicate that average yield


Fig. 5.

Farm profit ($ '000) 57-

4 7

37-

27

7 NET ON FARM PRICES /

/ WHEAT: 130 Si t /

WHEAT: 152 $/ t !

NO RIPPING (a) i

17 -- 120 240 360

Yield response to ripping (kg/ha)

(a) Profit from no ripping ia recorded for each wheat price.

Effect of wheat price and yield response to deep tillage on farm profit.

responses of around 560 kg/ha are possible, in practice farmers usually obtain yield improvements in the range 100-500 kg/ha with the average being around 370 kg/ha (Jarvis, 1990). Model results indicate that this average yield increase is sufficient to justify the deep tillage operation, although the profit improvements will not be as great as many hoped following early field experiments.

Optimal deep tillage practices

Having determined the yield increases necessary to justify deep tillage, the optimal method of deep tillage is now examined.

Table 2 shows details of the farm plans selected within the model with a single tractor and an l l-tine tillage implement. The set of optimal deep tillage practices changes substantially with variations in the yield response to deep tillage. Benefits from a yield response of 120 kg/ha do not outweigh the variable costs of deep tillage, so no deep tillage is selected at this level of yield response. At a yield response of 240 kg/ha, variable costs and part of the fixed cost associated with deep tillage are covered by benefits from deep tillage. Only for the cases where the yield response to deep tillage is 260 kg/ha or more are farm profits greater than when deep tillage is not possible.

The model calculates that the optimal rotations on yellow sandplain soil are 1 wheat:l lupin with a small area of continuous pasture. This re- mains true regardless of the availability of deep tillage options and the yield response to deep tillage.

Other results in Table 2 show that the preferred periods for deep


TABLE 2 Farm Plans for Various Yield Responses to Deep Tillage

No deep tillage

Wheat yield response to deep tillage (kg/ha)

120 240 360 480 600

Profit ($'000) 47-2 45.6 46.7 56.8 73.2 91.8

Rotations" PPPP 89 89 89 89 89 89 WL 2 161 2 161 1 027 0 0 0 WhL 0 0 1 124 1 405 271 271 WaL 0 0 0 432 1 566 1 566 WsL 0 0 0 324 324 324

Percentage of wheat area deep tilled 0 0 26 67 94 94

No. of days of deep tillage: (i) in summer 0 0 0 3 3 3 (ii) at seeding in

period I b 0 0 5 5 5 5 period 2 5 5 5 period 3 5 5

, P : pasture, W : wheat, L : lupin. Listed for each rotation is its area in hectares. WL: Wheat-lupin rotation with no deep tillage; WhL: each year half the area of wheat is deep tilled at seeding; WaL: each year all the area of wheat is deep tilled at seeding; WsL: deep tillage of the wheat phase of the rotation during summer. h Deep tillage in period 1 involves no yield penalty; in period 2 the yield penalty for delays to seeding is 15 kg/ha/day and in period 3 the yield penalty is 30 kg/ha/day.

tillage are in summer (af ter cyclonic rains or t hunde r s to rms) or dur ing the 5 days immedia te ly fol lowing comple t ion o f seeding (per iod 1). These per iods are p re fe r red because they avoid delays in sowing (and thus lower yields) caused by deep tillage dur ing the sowing p r o g r a m m e . As the yield response to deep tillage increases, it becomes m o re prof i tab le to deep till larger areas, even at the cost o f addi t iona l yield penalt ies for late sowing.

Shadow costs

A feature o f ma thema t i ca l p r o g r a m m i n g models such as M I D A S is tha t they genera te i n fo rma t ion a b o u t the relative prof i tabi l i ty o f activities no t included in the op t imal solut ion. This i n fo rma t ion is referred to as


TABLE 3 Shadow Costs of Some Alternative Rotations on the Yellow Sandplain Soils

Rotation Deep tillage schedule Shadow cost ( $/ha ) ~'

PPWW Deep till PPWW Deep till PPWW No deep WWWW Deep till WWWW Deep till WWWW No deep WL Deep till WL Deep till WL No deep WL Deep till WL Deep till WWL Deep till WL Deep till WWL No deep

pasture phase before wheat 29 one wheat phase after seeding 44 tillage 35 every second wheat phase after seeding • 70 every fourth wheat phase after seeding 68 tillage 60 half the wheat area every summer h Selected half the wheat area after seeding each year Selected tillage - 1.5 a quarter of the wheat area every summed' 2.6 a quarter of the wheat area after seeding each year Selected halt" the wheat area every summer/, 22 half the wheat area after seeding each year 22 tillage 13

,, The shadow costs are l\~r a farm with a single four-wheel-drive tractor, an l l-tine implement and 360 kg/ha cereal yield response to deep tillage. /, The opportunity for deep tillage in summer is based on expected rainfall events. In practice, there will be a range of deep tillage opportunities in summer.

' shadow costs ' . A shadow cost can be in te rpre ted in two ways. Firstly, it is the increase in prof i tabi l i ty required for an act ivi ty which is not cur-

rent ly selected to become par t o f the opt imal fa rm plan. Al ternat ively , it can be viewed as the reduc t ion in prof i t caused by the forced inclusion o f

the act ivi ty in the opt imal solut ion. No te that a l though they are t e rmed shadow costs, they do not involve a financial out lay. Ra th e r they are the reduc t ions in prof i t f rom not pursuing an opt imal strategy. Table 3 is a list o f sha dow costs o f var ious ro ta t ions closest in prof i tabi l i ty to those selected as par t o f the opt imal fa rm plan when wheat yield response to deep tillage is 360 kg/ha.

Large increases in per hectare profi t are required before ei ther cont inu- ous wheat or a wheat pas ture ro ta t ion replaces the w h e a t - l a p i n c rop ro- tat ion. Even if deep tillage occurs in the pas ture phase o f w h e a t - p a s t u r e ro ta t ions , the reby avoid ing compe t i t i on for use o f time, l abour and machinery at seeding, the prof i tabi l i ty o f these ro ta t ions still lags behind wheat lupin ro ta t ions , with or wi thou t deep tillage. Th e results show that in the yel low sandpla in soils, a d o p t i o n o f deep tillage m a y be re- stricted to its prof i tab le inclusion in w h e a t - l a p i n ro ta t ions .

304 A. K. Ahadi Ghadim, R. S. K#tgwell, D. J. Pannell

These results have implications for tillage research priorities. Deep tillage research should focus on wheat lupin rotations on the yellow sandplain soils. Unless substantial further improvements can be demon- strated from deep tillage research in wheat-pasture rotations, research ef- fort in this area is likely to be of little relevance to farmers since most will adopt the more profitable wheat lupin rotation.

Possible reasons for poor adoption

It has already been mentioned that adoption of deep tillage has not been as rapid or as widespread in the northern wheatbelt as originally anticipated. The results presented above suggest a number of possible reasons for this. Foremost of these is the yield response required for deep tillage to be economically justified. The break-even yield response is around 300 kg/ha in most situations. However, given significant year-to-year variability in the response, interference with the seeding programme, the possible need to hire and supervise extra labour and the substantial up-front capital investment required, farmers may require a considerably larger yield response than this to commit themselves to deep tillage. Something of the order of 400 kg/ha may be necessary. Farmer experience is that such a response is likely in some, but not all, years.

For those farmers who are risk averse, the variability of yield response to deep tillage will be of particular concern. They may not be prepared to accept the risk that in some years, profits may be lower in a deep tillage system than in a conventional system.

The wheat price was also shown to be an influence. While the EC and the USA continue to subsidize their wheat exports, the resulting suppres- sion of world prices will act as a disincentive to adoption of deep tillage in Western Australia.

Also some farmers may be either unprepared or unable to manage the scheduling of machinery and labour to undertake crop preparation, seeding and deep tillage operations. Even without the introduction of deep tillage, decision-making in late autumn is already difficult for farmers deciding over crucial issues of sheep management, crop preparation and crop sowing. Lastly, farmers experiencing cash flow or debt- servicing difficulties may not be prepared to risk a loss during their early years of adopting deep tillage, while they gain experience with the technology.

Despite these problems, a slowly growing number of farmers in the region are adopting deep tillage. Results from this analysis are being used to advise on when and how deep tillage can be most profitably practised.

Effects ~[ deep tillage on soil compaction 305

CONCLUSION

A main conclusion from the foregoing results is that adoption of deep tillage by an average farm in the northern wheat-growing region of Western Australia is profitable. This profitability, however, depends on the deep tillage occurring in wheat-lupin rotations in the yellow sandplain soils and the average yield response to deep tillage being at least 300 kg/ha. Given farmer and research experience that an average yield response of around 370 kg/ha is achievable, deep tillage seems destined to improve the profitability of many farms in the region.

Of course the calculation of 300 kg/ha as the break-even yield response depends on a range of assumptions in the model. However, the authors believe the model to be a good representation of almost all farms in the region. Furthermore, the break-even yield was found to be insensitive to changes in key assumptions.

The size of the increase in profits is shown to depend not only on the yield increase from deep tillage but also on the appropriate selection of rotations, frequency and timing of deep tillage and the type and size of deep tillage machinery. For example, a single tractor system is more profitable than a two tractor system for deep tillage, given the commonly experienced yield responses. Further, irrespective of the yield response the preferred periods for deep tillage are in summer (after cyclonic rains or thunderstorms) or during the 5 days immediately following completion of seeding.

ACKNOWLEDGEMENTS

The authors wish to thank A. Hagensen, P. Nelson and J. Weir for their encouragement, advice and provision of information. The authors also thank Ron Jarvis and farmers, D. Brinkley and C. Hume, for their interest and assistance and finally, acknowledge useful comments from a reviewer.

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306 A. K. Ahadi Ghadim, R. S. Kingwell, D. J. Pannell

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an economic evaluation of deep tillage to reduce soil compaction on crop-livestock farms in western...

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